Metadata-Version: 2.1
Name: xmca
Version: 0.1.0
Summary: Maximum Covariance Analysis in Python
Home-page: https://github.com/nicrie/xmca
Author: Niclas Rieger
Author-email: niclasrieger@gmail.com
License: GPL-3.0
Description: # Maximum Covariance Analysis in Python
        Maximum Covariance Analysis (MCA) maximises the temporal covariance between two different 
        data fields and is closely related to Principal Component Analysis (PCA) / Empirical 
        Orthogonal Function (EOF) analysis, which maximises the variance within a single data 
        field. MCA allows to extract the dominant co-varying patterns between two different data 
        fields.
        
        
        The module `xmca` works with `numpy.ndarray` and `xarray.DataArray` as input fields.
        
        ## Installation 
        ```
        pip install xmca
        ```
        
        ## Testing
        After cloning the repository
        ```
        python -m unittest discover -v -s tests/
        ```
        
        ## Core Features
        - Standard PCA/MCA
        - Rotated PCA/MCA
        	- Orthogonal Varimax rotation
        	- Oblique Promax rotation
        - Complex PCA/MCA (also known as Hilbert EOF analysis)
        	- Optimised Theta model extension
        - normalization of input data
        - latitude correction to compensate for stretched areas in higher latitutes
        
        
Keywords: mca
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
